Predictive Control System Visualization for Automatic Train Operation

1. A method comprising: obtaining, by a display computing system, a first data series of measured speed records and a second data series of predicted speed records at a visualization time during travel of a train, wherein: the measured speed records indicate measured speeds of the train over a measured span trailing the train in a direction of travel, the predicted speed records indicate predicted speeds of the train over a predicted span ahead of the train in the direction of travel, and the predicted speeds are determined by computing the measured speeds as inputs to a prediction model; determining an output interface length for the train based on a predetermined threshold length on an output interface of the display computing system; determining a position for a visualization of the train on the output interface based on the output interface length; and rendering, on a visualized space of the output interface in communication with the display computing system, a measured speed trend comprising: the measured speeds in a first region of a track topology comprising the position of the visualization of the train and a trailing distance; and a predicted speed trend comprising the predicted speeds in a second region of the track topology.

2. The method of claim 1, wherein the prediction model is learned by training a learning computing system prior to a departure of the train.

3. The method of claim 2, wherein the learning computing system is trained by inputting a sample dataset recording measured speeds of a train over time.

4. The method of claim 1, further comprising setting, by the display computing system, a width of the first region of a track topology and a width of the second region of the track topology each in output interface units; wherein the first region comprises a first portion of the track topology trailing the train in the direction of travel; and wherein the second region comprises a second portion of the track topology ahead of the train in the direction of travel.

5. The method of claim 1, further comprising reading, by the display computing system, each predicted speed record from the second data series which follows an index of the second data series corresponding to a current position of the train; wherein the predicted speeds rendered by the display computing system comprise each predicted speed record read by the display computing system.

6. The method of claim 1, further comprising rendering, by the display computing system, a speed limit trend in the first region and the second region; wherein the display computing system is configured to render the speed limit trend, the measured speeds trend, and the predicted speeds trend at a same scale relative to each other.

7. The method of claim 1, further comprising advancing, by the display computing system upon advancement of the visualization time, a span of travel positions mapped proportionally to a width of visualized space in the direction of travel, and re-mapping the advanced span of travel positions to the width of the visualized space.

8. A system comprising: one or more processors; and memory communicatively coupled to the one or more processors, the memory storing computer-executable modules executable by the one or more processors that, when executed by the one or more processors, perform associated operations, the computer-executable modules comprising: a data series obtaining module executable by the one or more processors to obtain a first data series of measured speed records and a second data series of predicted speed records at a visualization time during travel of a train, wherein: the measured speed records indicate measured speeds of a train over a measured span trailing the train in a direction of travel, the predicted speed records indicate predicted speeds of the train over a predicted span ahead of the train in the direction of travel, and the predicted speeds are determined by computing the measured speeds as inputs to a prediction model; determining an output interface length for the train based on a predetermined threshold length on an output interface of the display computing system; determining a position for a visualization of the train on the output interface based on the output interface length; and a rendering module executable by the one or more processors to render, on a visualized space of the output interface in communication with the system, a measured speed trend comprising: the measured speeds in a first region of a track topology comprising the position of the visualization of the train and a trailing distance; and a predicted speed trend comprising the predicted speeds in a second region of the track topology.

9. The system of claim 8, wherein the prediction model is learned by training a learning computing system prior to a departure of the train.

10. The system of claim 9, wherein the learning computing system is trained by inputting a sample dataset recording measured speeds of a train over time.

11. The system of claim 8, wherein the computer-executable modules further comprise a width setting module executable by the one or more processors to set a width of the first region of a track topology and a width of the second region of the track topology each in output interface units; wherein the first region comprises a first portion of the track topology trailing the train in the direction of travel; and wherein the second region comprises a second portion of the track topology ahead of the train in the direction of travel.

12. The system of claim 8, wherein the computer-executable modules further comprise a track profile recording module executable by the one or more processors to read each predicted speed record from the second data series which follows an index of the second data series corresponding to a current position of the train; wherein the predicted speeds rendered by the one or more processors comprise each predicted speed record read by the one or more processors.

13. The system of claim 8, wherein the rendering module is further executable by the one or more processors to render a speed limit trend in the first region and the second region; wherein the one or more processors are configured to render the speed limit trend, the measured speeds trend, and the predicted speeds trend at a same scale relative to each other.

14. The system of claim 8, wherein the rendering module is further executable by the one or more processors to advance, advancement of the visualization time, a span of travel positions mapped proportionally to a width of visualized space in the direction of travel, and re-mapping the advanced span of travel positions to the width of the visualized space.

15. A non-transitory computer-readable storage medium storing computer-readable instructions executable by one or more processors, that when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining, by a display computing system, a first data series of measured speed records and a second data series of predicted speed records at a visualization time during travel of a train, wherein: the measured speed records indicate measured speeds of a train over a measured span trailing the train in a direction of travel, the predicted speed records indicate predicted speeds of the train over a predicted span ahead of the train in the direction of travel, and the predicted speeds are determined by computing the measured speeds as inputs to a prediction model; determining an output interface length for the train based on a predetermined threshold length on an output interface of the display computing system; determining a position for a visualization of the train on the output interface based on the output interface length; and rendering, on a visualized space of the output interface in communication with the display computing system, a measured speed trend comprising: the measured speeds in a first region of a track topology comprising the position of the visualization of the train and a trailing distance; and a predicted speed trend comprising the predicted speeds in a second region of the track topology.

16. The non-transitory computer-readable storage medium of claim 15, wherein the prediction model is learned by training a learning computing system prior to a departure of the train.

17. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise setting, by the display computing system, a width of the first region of a track topology and a width of the second region of the track topology each in output interface units; wherein the first region comprises a first portion of the track topology trailing the train in the direction of travel; and wherein the second region comprises a second portion of the track topology ahead of the train in the direction of travel.

18. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise reading, by the display computing system, each predicted speed record from the second data series which follows an index of the second data series corresponding to a current position of the train; wherein the predicted speeds rendered by the display computing system comprise each predicted speed record read by the display computing system.

19. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise rendering, by the display computing system, a speed limit trend in the first region and the second region; wherein the display computing system is configured to render the speed limit trend, the measured speeds trend, and the predicted speeds trend at a same scale relative to each other.

20. The non-transitory computer-readable storage medium of claim 15, wherein the operations further comprise advancing, by the display computing system upon advancement of the visualization time, a span of travel positions mapped proportionally to a width of visualized space in the direction of travel, and re-mapping the advanced span of travel positions to the width of the visualized space.